On-line Capability Research Articles

Problems of copyright protection for gaming content

The article is devoted to a comprehensive study of the current issues of copyright protection of game content in the context of the rapid development of the video game industry. The study examines the peculiarities of legal protection of various elements of video games, including software code, audiovisual components, storylines and characters. It analyses the main challenges faced by developers and publishers in protecting their intellectual property rights, including unauthorised copying, piracy and unlicensed modifications. The author analyses the terminology of the industry, in particular, the concept of ‘video game’ which is widely used in international practice. At the same time, the author establishes that the term ‘computer game’ prevails in the European legal space. This terminological difference reflects regional peculiarities in approaches to classification and legal regulation of interactive entertainment products. Despite certain semantic differences, both terms cover electronic games operating on various digital platforms. With the development of online capabilities, video games have moved far beyond the isolated and closed subculture they once were. Video games now create an interactive virtual community and a thriving business that is part artistic expression, part sporting competition and part mass entertainment. A comprehensive analysis of a wide range of court precedents from different jurisdictions relating to the protection of rights to video content in the field of interactive entertainment was carried out. The study of these court decisions allowed the author to identify the key legal approaches and principles applied in cases of copyright infringement of video game elements. It is found that the differences between video games and other intellectual property objects have led to difficulties in determining the legal regime most appropriate to their specifics. Video games include a variety of constituent elements, many of which are eligible for legal protection. The growing involvement of video games in civilian circulation has given rise to the urgent problem of copyright protection for game content.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

E-nose detection of changes in volatile profile associated with early decay of ‘Golden Delicious’ apple by Penicillium expansum

The ability of an electronic nose system to differentiate alterations in the volatile organic compound (VOCs) profile caused by early spoilage of ‘Golden Delicious’ apples due to Penicillium expansum was thoroughly evaluated. Three strains of this mould species (M639, pe2280 and pe2278) were individually used to inoculate ‘Golden Delicious’ apples, resulting in four different batches: (i) M639, (ii) pe2280, (iii) pe2278 and (iv) a control group (inoculated with sterile water). Volatile compounds were identified and quantified by gas chromatography/mass spectrometry (GC/MS), and signals from an electronic nose system (E-nose) were recorded for each batch with three different growth diameters: early stage (10 mm), middle stage (20 mm) and late stage (40 mm). A strong correlation was observed between the responses from multiple E-nose biosensors and the aromatic profile associated with fungal alterations in apples. Using linear discriminant analysis (LDA) models based on E-nose sensor data from the specified batches, we successfully differentiated healthy and infected samples, specifically three distinct groups: control, M639+pe2280 and pe2278. Remarkably, the recognition rates for total external samples exceeded 87% and reached 97% for samples in the early stage of fungal infection. These results validate the online capability of E-nose technology for the non-destructive evaluation of apple storage processes.

State evaluation method for complex task network models

This paper focuses on the assessment of non-complete states in complex task networks and establishes an online capability boundary assessment model based on real-time feature parameters. By utilizing multivariate heterogeneous state features for data mining and pattern recognition, we delve into virtual sample generation technology using an overall diffusion trend approach. This starts by examining the validity of sample data and analyzing the relevance of data acquired within the feature space. Building on this foundation, the overall diffusion trend method is applied to generate virtual sample input that aligns with the sampling distribution characteristics of avionics system equipment. To address non-complete state evaluation in complex task networks, we design a comprehensive model. This model involves constructing a system signal flow block diagram and a structural diagram depicting the available capacity of subsystems and the overall system tasks. The establishment of unknown function relationships in signal connections is achieved through a fusion of neural network and fuzzy logic systems. Finally, an intelligent optimization algorithm is employed to determine system parameters. Utilizing this neuro-fuzzy system in simulation, we attain real-time system responses and evaluate the output regarding the available capacity of system tasks.

Towards Recognition of Human Actions in Collaborative Tasks with Robots: Extending Action Recognition with Tool Recognition Methods.

This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability.

A Text Analytics Framework for Supplier Capability Scoring Supported by Normalized Google Distance and Semantic Similarity Measurement Methods

AbstractThe unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, they do not lend themselves well to computational analysis. The objective of this work is to propose framework to enable automated classification and ranking of manufacturing suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics framework is supported by a formal thesaurus that uses Simple Knowledge Organization System (SKOS) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.

Bi-level energy management strategy for power-split plug-in hybrid electric vehicles: A reinforcement learning approach for prediction and control

The implementation of an energy management strategy plays a key role in improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). In this article, a bi-level energy management strategy with a novel speed prediction method leveraged by reinforcement learning is proposed to construct the optimization scheme for the inner energy allocation of PHEVs. First, the powertrain transmission model of the PHEV in a power-split type is analyzed in detail to obtain the energy routing and its crucial characteristics. Second, a Q-learning (QL) algorithm is applied to establish the speed predictor. Third, the double QL algorithm is introduced to train an effective controller offline that realizes the optimal power distribution. Finally, given a reference battery's state of charge (SOC), a model predictive control framework solved by the reinforcement learning agent with a novel speed predictor is proposed to build the bi-level energy management strategy. The simulation results show that the proposed method performs with a satisfying fuel economy in different driving scenarios while tracking the corresponding SOC references. Moreover, the calculation performance also implies the potential online capability of the proposed method.

Equilibrium flight theory-based orbit entry capability evaluation and guidance method of launch vehicles with thrust faults

<p indent="0mm">Considering the nonfatal engine faults (e.g., abnormal thrust) of a launch vehicle in the outer atmosphere flight phase, a method based on equilibrium flight theory for the evaluation of the vehicle’s orbit entry capability and continuous thrust guidance is proposed. First, the mechanism of mission failure caused by abnormal thrust fault is analyzed from the perspective of flight dynamics; on this basis, the simplified model of launch vehicle flight dynamics is established in the LVLH coordinate system, and the approximate analytical theory of equilibrium flight is provided. The conditions of an “equilibrium flight” and a “quasi-equilibrium flight” are further analyzed, and the differences between their guidance laws are compared. Next, the method and process of the online evaluation and decision-making of launch vehicles with an abnormal thrust are listed. Finally, numerical simulation is performed to reveal the relationship between thrust drop and orbit entry capability, as well as verify the correctness and practicability of the analytical theory of an equilibrium flight comprehensively. This simple and effective method integrates online capability evaluation and optimal orbit guidance, which can provide theoretical and technical support for rapid autonomous fault handling of launch vehicles with thrust faults.

Procedure to implement shear evolution in fast rolling models with online capability on the example of an aluminum flat rolling process

The rolling process induces a heterogeneous deformation over the rolling stock height. The causes are the frictional shear stress between the contacting surfaces and the roll gap geometry. They induce a complex material flow within the rolling stock describable by the shear evolution. The shear evolution has a significant impact on rolling values like grain size and crystallographic texture and therefore on the final material properties of the rolled product. Industry and academia use fast rolling models for flat rolling processes to predict the material properties due to their short computation time. The time advantage enables online applicability to adapt processes in real time or to evaluate the influence of different process conditions several times within seconds. However, these models have a limitation regarding the shear evolution. They do not consider all relevant influences or apply simplifying assumptions, valid only for specific rolling cases. This work presents a general approach to extend fast rolling models to consider the shear evolution without any restrictions to specific rolling cases. The approach derives the shear evolution as shear angle alpha evolution based on FE simulations. The shear angle alpha is a geometrical description and not influenced by rotations, which occur during the rolling process. This enables an enhanced and simple analysis of the material flow. The outcome is an extended model that completely describes the deformation along a deformation path and enables the calculation of each desired deformation value (strain, deformation gradient, velocity gradient, etc.).

MR-LINAC-Guided Adaptive Radiotherapy for Gastric MALT: Two Case Reports and a Literature Review

It is still very challenging to use conventional radiation therapy techniques to treat stomach tumors, although image-guided radiotherapy, mainly by kV X-ray imaging techniques, has become routine in the clinic. This is because the stomach is one of the most deformable organs, and thus it is vulnerable to respiratory motions, daily diet, and body position changes. In addition, X-ray radiographs and CT volumetric images have low contrast in soft tissues. In contrast, magnetic resonance imaging (MRI) techniques provide good contrast in images of soft tissues. The emerging MR-guided radiotherapy, based on the MR-LINAC system, may have the potential to solve the above difficulties due to its unique advantages. The real-time imaging feature and the high-contrast of soft tissues MR images provided by the MR-LINAC system have facilitated the therapeutic adaptive planning. Online learning capabilities could be used to optimize the automatic delineation of the target organ or tissue prior to each radiotherapy session. This could greatly improve the accuracy and efficiency of the target delineation in adaptive planning. In this clinical case report, we elaborated a workflow for the diagnosis and treatment of two patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma. One patient underwent MR-guided daily adaptive radiotherapy based on daily automated segmentation using the novel artificial intelligence (AI) technique for gastric delineation.

GenomeRxiv: a microbial whole-genome database and diagnostic marker design resource for classification, identification, and data sharing

genomeRxiv is a newly-funded US-UK collaboration to provide a public, web-accessible database of public genome sequences, accurately catalogued and classified by whole-genome similarity independent of their taxonomic affiliation. Our goal is to supply the basic and applied research community with rapid, precise and accurate identification of unknown isolates based on genome sequence alone, and with molecular tools for environmental analysis. The DNA sequencing revolution enabled the use of cultured and uncultured microorganism genomes for fast and precise identification. However, precise identification is impossible without 1. reference databases that precisely circumscribe classes of microorganisms, and label these with their uniquely-shared characteristics 2. fast algorithms that can handle the volumes of genome data Our approach integrates the highly-resolved classification framework of Life Identification Numbers (LINs) with the speed and computational efficiency of sourmash and k-mer hashing algorithms, and the precision and filtering of average nucleotide identity (ANI). We aim to construct a single genome-based indexing scheme that extends from phylum to strain, enabling the unique and consistent placement of any sequenced prokaryote genome. genomeRxiv includes protocols for confidentiality, allowing groups to identify and announce the identities of newly-sequenced organisms without sharing genome data directly. This protects communities working with commercially- and ethically-sensitive organisms (e.g. production engineering strains, potential bioweapons, and to enable benefit sharing with indigenous communities). genomeRxiv will also provide online capability to design molecular diagnostic tools for metabarcoding and qPCR, to enable tracking of specific groupings of bacteria directly in the environment.

Service Quality Measurement in Indian Hotels:Factor Structure &amp; Scale Validation

With the rapid growth of hospitality industry, many hoteliers have developed their online capabilities and evolved into competitive dual-channel service providers. In such a case, strategic consumers have more options to book a hotel enabling them to choose the best time and the best channel. This study aims at measuring service quality being offered by traditional hotels in Allahabad with the help of SERVPERF model. Besides, it also attempts to investigate the dimensionality, item structure and psychometric properties of the SERVPERF scale in Indian hotel industry. Findings reveal that SERVPERF scale fails to hold all the original items and hence, fails to confirm the standard item assemblage within the backdrop of hotel industry. In all, seven dimensions have emerged contrary to the original five-dimensions namely-tangibles, assurance, reliability, responsiveness and empathy. Besides this, there were also a number of considerable issues which form the basis for the current research. The outcome of the current study will be of help to managers of Indian hotel industry to analyze their service offerings with reference to the suggested seven key factors and accordingly develop strategies to highlight and prioritize them.

Low-frequency ultrasound as a tool for quality control of soft-bodied raw ewe's milk cheeses

The relationship between the quality attributes and the ultrasound parameters of Protected Designation of Origin (PDO) soft-bodied cheeses from “Torta del Casar” and “Queso de la Serena” was studied. Cheese samples were taken from six different dairy industries (three for each PDO) in two different seasons. Ultrasound parameters related to velocity, attenuation and frequency were calculated and correlated with the physico-chemical, microbiological, rheological and sensorial properties of the cheeses. Several ultrasonic parameters showed a high correlation with changes in the physico-chemical, textural and sensorial properties of this product, which were partially associated with microbial activity. Among these, stand out the negative correlation of the sensorial descriptor “firmness” with bulk modulus ( κ ). The predictive models achieved by the multivariate linear regression method (MLR) showed the highest correlation coefficients for firmness by texture compression analysis (TCA) and the sensory descriptor “intensity”, including as the explanatory variables for both models, fast Fourier transform (FFT) frequencies and attenuation parameters. This work highlights the on-line capability of ultrasound for non-destructive quality assessment of this traditional and heterogeneous soft cheese based on the predictive potential of several secondary ultrasonic parameters. • Soft-bodied raw ewe's milk cheeses granted the PDO label were evaluated. • Relationships among cheese quality attributes and ultrasound parameters were studied. • Ultrasonic parameters showed correlations with physico-chemical, textural and sensorial properties. • A predictive model was developed by the multivariate linear regression method. • Ultrasound parameters are useful tools for quality control of soft-bodied cheeses.

198 A Novel ‘Virtual Simulation’ for The Advanced Life Support Group, Making a Dream a Reality: A Beginner’s Guide

In the UK, it is a requirement for staff involved in paediatric critical care to remain up to date with advanced paediatric life support (APLS). To do so there is an expectation to participate in face-to-face courses on a four-yearly basis. The Advanced Life Support Group (ALSG), an organization dedicated to supporting professional education, sought to add to their resources in line with advances in online capabilities. With stakeholder input, the goal emerged to develop an engaging, interactive and entirely novel educational product. Here we present the pilot stages of our project.The aim of this project was to produce an educationally effective and novel product that learners of APLS would engage with and enjoy.Stage 1 involved determining clearly defined learning objectives mapped to the APLS curriculum. At a round table with stakeholders, educationalists, and a technologist an outline of the project was formed and the scenario of a sick baby with bronchiolitis chosen for a pilot. Informed by literature on serious game design, a branching narrative was created. Stage 2 involved collating resources. A photoshoot at Leicester Royal Infirmary A&amp;E (Accident and Emergency) created a set of images. A video shoot at the Royal London Hospital A&amp;E generated a series of videos. These were then edited and used to create a Microsoft PowerPoint slide set. Voiceovers to text, sound effects to add a hospital atmosphere and questions were then added. Stage 3 involved taking this draft and translating it into an interactive final product utilizing Articulate Software. This enabled its usage across smartphone, desktop, and laptop devices. Testing followed with anonymous online feedback informed by the 7Is framework We launched the pilot version at the RCPCH (Royal College of Paediatrics and Child Health) Conference 2021. Feedback was collected from delegates and continues to be collected via online participants. It is hoped that ongoing quality improvement cycles will assist in assuring a finished fully functional online virtual advanced paediatric life support simulation for release in 2022. Further stakeholder review is pending. Thus far it has been met with universal approval, i.e. all learners questioned expressed that they would like to utilize this novel style of education again. The average duration to complete the virtual simulation was 15 minutes. All learners rated their knowledge and skills in APLS to be either unchanged/revised or improved. Finally, interactivity online was felt to need improvement by most participants. Interactivity is key if high levels of engagement are to be achieved. Future testing will determine whether any educational impact is maintained across time. The current pilot version can be accessed at the following webpage

Teaching readiness and capability of island school teachers in the district of Concepcion Iloilo

&lt;em&gt;Teaching readiness and capability of teachers’ mirror the quality of students’ achievements. The determination of readiness and capability of teachers particularly in island schools identified the extent of teachers’ competence despite online skill inadequacies amidst the pandemic. The study included a relationship of personal factors that have bearing on teachers’ readiness and capability. Descriptive-correlational research design was used with simple random sampling. One hundred seventy-two (172) teachers provided the needed data. Data were gathered through a researcher-made questionnaire. The statistical tools used were descriptive statistics, t-test and One-Way ANOVA for significant difference and Pearson r for relationship of teaching readiness and level of teachers’ capability. The findings highlighted teachers’ teaching readiness as high particularly in the preparation of instructional materials and average as to technological requirements such as in place internet access, with computers, laptops, other digital gadgets and delivery of students’ academic needs using technology. While the teachers were highly capable in online skills like creating models, guides, online assessment materials and use of articles, videos and infographics. In the new normal class management, teachers demonstrated very high capability in stimulating student’s curiosity in learning by integrating real life situations, detailed modules and providing an engaging environment through oral communication. No significant differences were found on the degree of teaching readiness and level of teachers’ capability when respondents were grouped according to their selected profile. However, teaching readiness has significant bearing on teachers’ online capability. &lt;/em&gt;

Help-seeking and masculinity in opposition: A quantitative study investigating the potential of coaching to increase men’s engagement in help-seeking

IntroductionMen’s help-seeking behaviours have been highlighted as a major issue in men’s mental health (Robertson &amp; Baker, 2017) with widespread research indicating that aspects of masculine identity hinder men from seeking help with mental health and wellbeing issues (e.g. Berger et al., 2013). Although coaching is not a therapeutic intervention, the approach is aimed at facilitating positive change. As the language around coaching is more closely aligned to masculine identity it is suggested that there is potential to engage more men in seeking help.AimTo investigate the potential of coaching for formal help-seeking among men.MethodsAn online quantitative survey was conducted among 118 men (18+ years), who rated the likelihood of engagement in specified forms of help-seeking in three case studies. The vignette version of the General Help-Seeking Questionnaire (GHSQ-V) was utilised. Data were analysed using SPSS Version 25.Results:Self-help/online, such as books and online resources, was found to be the most likely form of formal help men in this study would seek (M=3.94, SD=1.90). However, coaching was reported as the most likely face-to-face formal source of help (M=3.73, SD=1.39), with a paired sample t-test indicating significantly greater likelihood of attending coaching than the other formal forms of help investigated (p=0.000). Attending coaching was reported as significantly more likely than attending counselling/psychotherapy in all case studies.ImplicationsThis study found that coaching is more appealing to men than traditional forms of help-seeking, thus indicating the potential of coaching for formal help-seeking among men. Coaching, in conjunction with its online capabilities, may have additional potential to engage more men in help-seeking, thereby positively impacting on men’s mental health.

Axial Shuffling on Pebble Bed Reactor using PRAKTIK 3D-HTR

With moving fuel, the pebble bed reactor (PBR) provides flexibility in the fuel management process due to the capability of online fuel refueling. This capability allows the reactor to operate at any given time without the need to shut down for refueling. The complexity of the depletion and burnup analysis requires the problem to be solved with sophisticated and robust computer codes that can handle the fuel shuffling. Since the fuel refueling is conducted from top to bottom, the shuffling and fuel movement in the axial direction should be modeled with acceptable accuracy. The purpose of the simulation is to obtain the equilibrium or even a critical condition of the reactor. The model used is based on the simplified pebble bed reactor with 200 MWt of thermal reactor power, 3 meters of core diameter, and 10 meters of core height. To model the axial shuffling on the reactor, a neutronic computer code called PRAKTIK 3D-HTR is used. The code utilizes the diffusion method in a three-dimensional cylindrical geometry to model the neutronic phenomena in the reactor. Moreover, PRAKTIK 3D-HTR is equipped with the burnup calculation and depletion analysis to be able to handle fuel movement. Finally, the axial shuffling mechanism is implemented using the once-through-then-out (OTTO) method. Implementing this method to the reactor, an equilibrium condition can be obtained. In this condition, the reactor condition in terms of criticality and flux shape is relatively constant. The critical condition can also be searched using PRAKTIK 3D-HTR to obtain the condition when the multiplication factor is equal to unity. The criticality search is conducted by changing the fuel movement speed. If the multiplication factor is less than 1, then the shuffling speed needs to be increased. Otherwise, if it is more than 1, the shuffling speed will be decreased.

Identifying Technical Requirements for a Mobile Business Analytics Application

This study identifies various technical requirements for business analytics applications designed and optimized for mobile devices. Such applications would enable the mobile workforce to gain business insights regardless of their physical locations. In order for the mobile workforce to be efficient and effective, they need to be able to use the browsers and applications designed specifically for the mobile devices to access data, similar to desktop computers. Based on the research, such factors as online and off-line data analysis capabilities, data visualization capabilities, and security issues appear to be important factors to be addressed carefully by developers.

Product Abundance, Product Delivery and Price of Product as Determinants of Online Business Profit Performance

With the increasing number of small and medium enterprises (SMEs) in Malaysia, most young entrepreneurs have started to use the internet as their business platform. However, the issue faced by these SMEs is maintaining their profit performance and long-term business sustainability. In maintaining their profit performance, entrepreneurs need to ensure they have product abundance, provide the best service delivery and offer the best price in the market. Hence, the objective of this research was to examine the relationship between an online business performance with the three main pillars of online business - product abundance, product delivery and product price. 210 registered SMEs located in the Klang Valley were chosen as the survey respondents. The result indicates that, product abundance and product price have a significant relationship with the performance, while product delivery had no significant relationship. This study proved that price of products offered by online business is crucial in maintaining customers’ satisfaction that is closely related to sales and performance. The findings of the study assists the entrepreneurs in developing their business while contributing to the nation’s economy when more potential SMEs’ businesses with an online capability are able to penetrate into the international market and compete globally. Keywords: online business, product abundance, product delivery, price of product, profit performance

An Adaptive Prognostic Approach for Partially Observable Degrading Products With Random Shocks

This paper proposes an adaptive remaining useful life (RUL) estimation method for partially observable degrading products with time-varying random shocks. In the modeling aspect of the proposed method, a shock degradation model is proposed to characterize the degradation process of the product, in which the continuous degradation process is described by the Wiener process, and the random shock process with a time-varying intensity is described by the non-homogeneous compound Poisson process (NHCPP). In the proposed model, to characterize the effect of random shocks on the degradation process, the degradation rate is related to the number of shocks and the magnitude of cumulative shocks. In the estimating aspect, we utilize a strong tracking filtering (STF) algorithm to estimate partially observable degradation states and a two-step expectation conditional maximization (ECM) algorithm to estimate the model parameters. In the prognostic aspect, the approximated analytical RUL distribution under the concept of the first hitting time (FHT) is obtained by incorporating the unit-to-unit variability and the uncertainty of the partially observable degradation state estimation from observations into the RUL estimation. As such, the RUL distribution can be updated according to the latest available degradation observation, thereby realizing the adaptive RUL estimation. Finally, the accuracy and effectiveness of the proposed approach is verified by a numerical example and a practical case study for the furnace wall, which provides higher estimation accuracy and better online capability than existing approaches.